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Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors.

Emily C Wheeler, Anthony Q Vu, Jaclyn M Einstein, Matthew DiSalvo, Noorsher Ahmed, Eric L Van Nostrand, Alexander A Shishkin, Wenhao Jin, Nancy L Allbritton, Gene W Yeo

Nature methods · 2020 · Vol. 17 (6) · pp. 636-642

Abstract

Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR-Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein-RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens.

Linked datasets 1

GSE139815 GSE · via ncbi_elink
GEO ↗

Pooled CRISPR screens with imaging on microRaft arrays reveals stress granule-regulatory factors

Homo sapiens

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Potentially related datasets 1

These accessions were text-mined from the PMC full text. They may be referenced for comparison, cited from other studies, or otherwise mentioned without being primary data for this paper.

Accession Source Title
GSE139815 GSE · GEO GEO ↗ Pooled CRISPR screens with imaging on microRaft arrays reveals stress granule-regulatory factors

Analysis pipelines 1

Pipeline Assay Steps Dataset
GSE139815 Data Processing geo_data_processing — 2 GSE139815
Funding 10 grants
Grant Funder
R01 HG004659 NHGRI NIH HHS (United States)
U54 CA209891 NCI NIH HHS (United States)
R01 NS103172 NINDS NIH HHS (United States)
T32 GM145427 NIGMS NIH HHS (United States)
T32 GM008666 NIGMS NIH HHS (United States)
P50 GM085764 NIGMS NIH HHS (United States)
K99 HG009530 NHGRI NIH HHS (United States)
F31 CA217173 NCI NIH HHS (United States)
F31 CA206233 NCI NIH HHS (United States)
R01 EY024556 NEI NIH HHS (United States)